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Identifying multimorbid patients with high care needs: a study based on electronic medical record data.
Heins, M., Korevaar, J., .Schellevis, F., Rijken, M. Identifying multimorbid patients with high care needs: a study based on electronic medical record data. European Journal of General Practice: 2020, 26(1), 189-195
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Background
Patients with multimorbidity who frequently contact the general practice, use emergency care or have unplanned hospitalisations, may benefit from a proactive integrated care intervention. General practitioners are not always aware of who these ‘high need’ patients are. Electronic medical records are a potential source to identify them.
Objectives
To find predictors of high care needs in general practice electronic medical records of patients with multimorbidity and assess their predictive value.
Methods
General practice electronic medical records of 245,065 patients with 2 chronic diseases were linked to hospital claims data. Probit regression analysis was conducted to predict:
i) having at least 12 general practice contacts per year,
ii) emergency department visit(s),
iii) unplanned hospitalisation(s).
Predictors were patients’ age, sex, morbidity, health services and medication use in the previous year.
Results
11% of multimorbid patients had 12 general practice contacts, which could be reliably predicted by the number of contacts in the previous year (PPV 42%). The model containing all predictors had only slightly better predictive value (PPV 44%). Emergency department visits and unplanned hospitalisations (12% and 7% of multimorbid patients, respectively) could be predicted less accurately (PPV 27% and 20%). Those with frequent contact with the general practice hardly overlapped with ED visitors (29%) or persons with unplanned hospitalisations (17%).
Conclusion
Among multimorbid populations various ‘high need’ groups exist. Patients with high needs for general practice care can be identified by their previous use of general practice care. To identify frequent ED visitors and persons with unplanned hospitalisations, additional information is needed.
Patients with multimorbidity who frequently contact the general practice, use emergency care or have unplanned hospitalisations, may benefit from a proactive integrated care intervention. General practitioners are not always aware of who these ‘high need’ patients are. Electronic medical records are a potential source to identify them.
Objectives
To find predictors of high care needs in general practice electronic medical records of patients with multimorbidity and assess their predictive value.
Methods
General practice electronic medical records of 245,065 patients with 2 chronic diseases were linked to hospital claims data. Probit regression analysis was conducted to predict:
i) having at least 12 general practice contacts per year,
ii) emergency department visit(s),
iii) unplanned hospitalisation(s).
Predictors were patients’ age, sex, morbidity, health services and medication use in the previous year.
Results
11% of multimorbid patients had 12 general practice contacts, which could be reliably predicted by the number of contacts in the previous year (PPV 42%). The model containing all predictors had only slightly better predictive value (PPV 44%). Emergency department visits and unplanned hospitalisations (12% and 7% of multimorbid patients, respectively) could be predicted less accurately (PPV 27% and 20%). Those with frequent contact with the general practice hardly overlapped with ED visitors (29%) or persons with unplanned hospitalisations (17%).
Conclusion
Among multimorbid populations various ‘high need’ groups exist. Patients with high needs for general practice care can be identified by their previous use of general practice care. To identify frequent ED visitors and persons with unplanned hospitalisations, additional information is needed.
Background
Patients with multimorbidity who frequently contact the general practice, use emergency care or have unplanned hospitalisations, may benefit from a proactive integrated care intervention. General practitioners are not always aware of who these ‘high need’ patients are. Electronic medical records are a potential source to identify them.
Objectives
To find predictors of high care needs in general practice electronic medical records of patients with multimorbidity and assess their predictive value.
Methods
General practice electronic medical records of 245,065 patients with 2 chronic diseases were linked to hospital claims data. Probit regression analysis was conducted to predict:
i) having at least 12 general practice contacts per year,
ii) emergency department visit(s),
iii) unplanned hospitalisation(s).
Predictors were patients’ age, sex, morbidity, health services and medication use in the previous year.
Results
11% of multimorbid patients had 12 general practice contacts, which could be reliably predicted by the number of contacts in the previous year (PPV 42%). The model containing all predictors had only slightly better predictive value (PPV 44%). Emergency department visits and unplanned hospitalisations (12% and 7% of multimorbid patients, respectively) could be predicted less accurately (PPV 27% and 20%). Those with frequent contact with the general practice hardly overlapped with ED visitors (29%) or persons with unplanned hospitalisations (17%).
Conclusion
Among multimorbid populations various ‘high need’ groups exist. Patients with high needs for general practice care can be identified by their previous use of general practice care. To identify frequent ED visitors and persons with unplanned hospitalisations, additional information is needed.
Patients with multimorbidity who frequently contact the general practice, use emergency care or have unplanned hospitalisations, may benefit from a proactive integrated care intervention. General practitioners are not always aware of who these ‘high need’ patients are. Electronic medical records are a potential source to identify them.
Objectives
To find predictors of high care needs in general practice electronic medical records of patients with multimorbidity and assess their predictive value.
Methods
General practice electronic medical records of 245,065 patients with 2 chronic diseases were linked to hospital claims data. Probit regression analysis was conducted to predict:
i) having at least 12 general practice contacts per year,
ii) emergency department visit(s),
iii) unplanned hospitalisation(s).
Predictors were patients’ age, sex, morbidity, health services and medication use in the previous year.
Results
11% of multimorbid patients had 12 general practice contacts, which could be reliably predicted by the number of contacts in the previous year (PPV 42%). The model containing all predictors had only slightly better predictive value (PPV 44%). Emergency department visits and unplanned hospitalisations (12% and 7% of multimorbid patients, respectively) could be predicted less accurately (PPV 27% and 20%). Those with frequent contact with the general practice hardly overlapped with ED visitors (29%) or persons with unplanned hospitalisations (17%).
Conclusion
Among multimorbid populations various ‘high need’ groups exist. Patients with high needs for general practice care can be identified by their previous use of general practice care. To identify frequent ED visitors and persons with unplanned hospitalisations, additional information is needed.
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